Computational modeling of human anatomy facilitates an understanding of the anatomical behavior that typifies different physiological conditions. While state-of-the-art imaging techniques can allow a physician to visualize anatomic behavior, the state-of-the-art technology that accurately images the complex movement of the heart and lung is often too expensive to be widely adopted. Moreover, cardiac motion, e.g., the deformation of the heart, which is an intricate process and unrelated to the breathing cycle, may appear as noise in CT-based or radiographic breathing motion measurements. Consequently, the accuracy of mathematical models that describe breathing motion will also be degraded by seemingly random heart motion. One way of addressing this issue is to remove lung motion from images by making patients hold their breath. While this approach stops the patient's breathing motion, crucial information on lung health may be hard to identify in these breath-hold images.